An Incremental Learning-Based Majority Voting Framework for Network Intrusion Detection System

In the past, Intrusion Detection has played a crucial role in securing networked infrastructures against malicious attacks. Initially dominated by signature-based methods, early IDS frameworks excelled in identifying known threats but struggled with zero-day or polymorphic attacks. To address this limitation, anomaly-based systems emerged to detect unknown threats by analyzing deviations from normal behavior. Nevertheless, these systems frequently encountered high false-positive rates and lacked contextual precision. The introduction the advancement progress in machine learning (ML) has enabled the design of intelligent IDS capable of learning from evolving attack patterns. This study introduces an integrated Intrusion Detection System design which integrates signature-based and anomaly-based detection, strengthened by a majority-voting ensemble machine learning model. Leveraging public datasets like NSL-KDD and CICIDS2017, the system undergoes thorough Data preprocessing, feature extraction, and classification using (Support Vector Machine) SVM, Decision Tree, and Random Forest algorithms. Each model plays a role in overall prediction, enhancing robustness and accuracy through majority voting. Empirical findings reveal the suggested idea hybrid the model obtains a precision rate of over 94%, with precision and recall consistently exceeding 90% across key attack categories. The modular design allows for deployment in enterprise networks and real-time systems, providing scalability and low-latency performance. Moreover, the framework effectively tackles challenges such as dataset imbalance, feature noise, and model generalization. This study emphasizes the viability of implementing machine learning-based IDS solutions in contemporary digital infrastructures, combining detection accuracy with operational feasibility.